Authors :
Y V NageshMeesala; N Sai Sankar; M Abhishek; P Rahul
Volume/Issue :
Volume 7 - 2022, Issue 10 - October
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3TyFsxp
DOI :
https://doi.org/10.5281/zenodo.7319071
Abstract :
- Cardio vascular diseases are one of the major
causes of death globally.It is very important tofind a
precise and well founded approach to automate the
accomplished work and thus carrying out effective
management. Many researchers used several data
mining techniques to understand and help in diagnose
heart disease. In order to decrease the deaths from heart
disease, you must have a fast and precise detection
technique. Early prediction can help people change their
lifestyle. It also ensures proper medical treatment if
needed. In order to drop down the death rate of heart
diseases, a rapid and precise techniquesare needed. The
proposed work predicts the possibilities of heart diseases
by implementing various number of data mining
techniques such as logistic regression, K nearest, decision
trees, support vector machine. A web based system is
developed in this paper, that can determine whether a
person is likely to get affected with heart disease or not
based his health factors. It is found that the Support
Vector Machine achieved a maximum accuracy of
86.76% against other implemented ML algorithms.
Keywords :
Machine Learning, Health care, Cardio Vascular Diseases, Modelling and Training, Cardiologist.
- Cardio vascular diseases are one of the major
causes of death globally.It is very important tofind a
precise and well founded approach to automate the
accomplished work and thus carrying out effective
management. Many researchers used several data
mining techniques to understand and help in diagnose
heart disease. In order to decrease the deaths from heart
disease, you must have a fast and precise detection
technique. Early prediction can help people change their
lifestyle. It also ensures proper medical treatment if
needed. In order to drop down the death rate of heart
diseases, a rapid and precise techniquesare needed. The
proposed work predicts the possibilities of heart diseases
by implementing various number of data mining
techniques such as logistic regression, K nearest, decision
trees, support vector machine. A web based system is
developed in this paper, that can determine whether a
person is likely to get affected with heart disease or not
based his health factors. It is found that the Support
Vector Machine achieved a maximum accuracy of
86.76% against other implemented ML algorithms.
Keywords :
Machine Learning, Health care, Cardio Vascular Diseases, Modelling and Training, Cardiologist.
